General bounds on statistical query learning and PAC learning withnoise via hypothesis boosting
Aslam, J.A.
Decatur, S.E.
Lab. for Comput. Sci., MIT, Cambridge, MA;
This paper appears in: Foundations of Computer Science, 1993. Proceedings., 34th Annual Symposium on
Publication Date: 3-5 Nov 1993
On page(s): 282-291
Meeting Date: 11/03/1993 - 11/05/1993
Location: Palo Alto, CA, USA
ISBN: 0-8186-4370-6
References Cited: 18
INSPEC Accession Number: 4851331
Digital Object Identifier: 10.1109/SFCS.1993.366859
Current Version Published: 2002-08-06
Abstract
We derive general bounds on the complexity of learning in the
statistical query model and in the PAC model with classification noise.
We do so by considering the problem of boosting the accuracy of weak
learning algorithms which fall within the statistical query model. This
new model was introduced by M. Kearns (1993) to provide a general
framework for efficient PAC learning in the presence of classification
noise
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